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The global rise in myopia, particularly among children and adolescents in China, underscores the inadequacy of current prevention strategies, indicating that conventional screening and education alone are insufficient to curb the prevalence. Integrating personalized myopia prediction into routine care may enhance risk awareness, promote proactive prevention, and improve adherence to medical advice, ultimately reducing the future burden of high myopia.
A myopia prediction system based on artificial intelligence was previously developed, accurately predicting future high myopia risk using efficient, robust, and easily accessible predictive factors, including age, spherical equivalent, and the annual progression of spherical equivalent. This study aims to conduct a prospective, one-year, cluster randomized controlled clinical trial to investigate the effectiveness of this prediction system in preventing and controlling myopia in school-aged children.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experimental Arm | Experimental | At baseline and six months, feedback on ophthalmic examination results and future high myopia risk at 18 years of age using the myopia prediction system |
|
| Control Arm | Active Comparator | At baseline and six months, feedback on ophthalmic examination results. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Feedback on Predicted High Myopia Risk at Age 18 Using the Myopia Prediction System | Other | At baseline and six months, participants will be provided with the results of their predicted risk of high myopia at age 18 based on the myopia prediction system. |
| Measure | Description | Time Frame |
|---|---|---|
| Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System | At the end of the one-year study, the Myopia Prediction System will be used to predict whether students will develop high myopia at age 18 in both the intervention and control groups. The Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System is calculated as the total number of students in each group predicted to develop high myopia by age 18, divided by the total number of students in the respective group. | 1 year |
| Cumulative Clinical Visit Rate for Myopia Prevention and Control | The Cumulative Clinical Visit Rate Proportion of Clinical Visits for Myopia Prevention and Control is the proportion of students in the intervention or control group who visited a hospital or clinic for myopia-related care (e.g., refractive exams and treatment) at least once within three months of either intervention. It is calculated as the number of students in each group who attended a clinical visit within three months of at least one intervention, divided by the total number of students in the respective group. | Within 3 months after each intervention |
| Measure | Description | Time Frame |
|---|---|---|
| Myopia Incidence Rate | 1-year myopia incidence rate = number of new myopia cases within one year / number of non-myopic cases at baseline * 100% | 1 year |
| Changes in Spherical Equivalent | Change in spherical equivalent (non-cycloplegic autorefraction) will be calculated |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yahan Yang, M.D., Ph.D | Contact | +86 15521013933 | yah.yang39@qq.com | |
| Xinwei Chen, M.D. | Contact | +86 13535382011 | cxw20000709@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Haotian Lin, M.D., Ph.D | Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhongshan Ophthalmic Center, Sun Yat-sen University | Guangzhou | Guangdong | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30399150 | Background | Lin H, Long E, Ding X, Diao H, Chen Z, Liu R, Huang J, Cai J, Xu S, Zhang X, Wang D, Chen K, Yu T, Wu D, Zhao X, Liu Z, Wu X, Jiang Y, Yang X, Cui D, Liu W, Zheng Y, Luo L, Wang H, Chan CC, Morgan IG, He M, Liu Y. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med. 2018 Nov 6;15(11):e1002674. doi: 10.1371/journal.pmed.1002674. eCollection 2018 Nov. |
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| Feedback on Ophthalmic Examinations | Other | At baseline and six months, participants will be provided with the results of their ophthalmic examinations. |
|
| 1 year |
| Screen Time | Daily usage time of electronic devices (computer/smartphone/tablet computer) will be calculated | 1 year |
| Outdoor Activity Time | Daily outdoor activity time will be calculated | 1 year |
| ID | Term |
|---|---|
| D009216 | Myopia |
| ID | Term |
|---|---|
| D012030 | Refractive Errors |
| D005128 | Eye Diseases |
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